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Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels

Overview of attention for article published in PLOS ONE, April 2013
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Title
Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0062982
Pubmed ID
Authors

Serdar Bozdag, Aiguo Li, Gregory Riddick, Yuri Kotliarov, Mehmet Baysan, Fabio M. Iwamoto, Margaret C. Cam, Svetlana Kotliarova, Howard A. Fine

Abstract

Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Germany 1 1%
Finland 1 1%
Ukraine 1 1%
United States 1 1%
Unknown 73 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Researcher 17 22%
Student > Bachelor 9 11%
Professor > Associate Professor 5 6%
Student > Master 5 6%
Other 15 19%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 23%
Medicine and Dentistry 17 22%
Biochemistry, Genetics and Molecular Biology 13 16%
Neuroscience 6 8%
Computer Science 4 5%
Other 7 9%
Unknown 14 18%